The dataclass() decorator examines the class to find
fields. A field is defined as class variable that has a
type annotation. With two
exceptions described below, nothing in dataclass()
examines the type specified in the variable annotation.

The order of the fields in all of the generated methods is the
order in which they appear in the class definition.

The dataclass() decorator will add various “dunder” methods to
the class, described below. If any of the added methods already
exist on the class, the behavior depends on the parameter, as documented
below. The decorator returns the same class that is called on; no new
class is created.

If dataclass() is used just as a simple decorator with no parameters,
it acts as if it has the default values documented in this
signature. That is, these three uses of dataclass() are
equivalent:

repr: If true (the default), a __repr__() method will be
generated. The generated repr string will have the class name and
the name and repr of each field, in the order they are defined in
the class. Fields that are marked as being excluded from the repr
are not included. For example:
InventoryItem(name='widget',unit_price=3.0,quantity_on_hand=10).

eq: If true (the default), an __eq__() method will be
generated. This method compares the class as if it were a tuple
of its fields, in order. Both instances in the comparison must
be of the identical type.

order: If true (the default is False), __lt__(),
__le__(), __gt__(), and __ge__() methods will be
generated. These compare the class as if it were a tuple of its
fields, in order. Both instances in the comparison must be of the
identical type. If order is true and eq is false, a
ValueError is raised.

unsafe_hash: If False (the default), a __hash__() method
is generated according to how eq and frozen are set.

__hash__() is used by built-in hash(), and when objects are
added to hashed collections such as dictionaries and sets. Having a
__hash__() implies that instances of the class are immutable.
Mutability is a complicated property that depends on the programmer’s
intent, the existence and behavior of __eq__(), and the values of
the eq and frozen flags in the dataclass() decorator.

By default, dataclass() will not implicitly add a __hash__()
method unless it is safe to do so. Neither will it add or change an
existing explicitly defined __hash__() method. Setting the class
attribute __hash__=None has a specific meaning to Python, as
described in the __hash__() documentation.

If __hash__() is not explicit defined, or if it is set to None,
then dataclass()may add an implicit __hash__() method.
Although not recommended, you can force dataclass() to create a
__hash__() method with unsafe_hash=True. This might be the case
if your class is logically immutable but can nonetheless be mutated.
This is a specialized use case and should be considered carefully.

Here are the rules governing implicit creation of a __hash__()
method. Note that you cannot both have an explicit __hash__()
method in your dataclass and set unsafe_hash=True; this will result
in a TypeError.

If eq and frozen are both true, by default dataclass() will
generate a __hash__() method for you. If eq is true and
frozen is false, __hash__() will be set to None, marking it
unhashable (which it is, since it is mutable). If eq is false,
__hash__() will be left untouched meaning the __hash__()
method of the superclass will be used (if the superclass is
object, this means it will fall back to id-based hashing).

frozen: If true (the default is False), assigning to fields will
generate an exception. This emulates read-only frozen instances. If
__setattr__() or __delattr__() is defined in the class, then
TypeError is raised. See the discussion below.

For common and simple use cases, no other functionality is
required. There are, however, some dataclass features that
require additional per-field information. To satisfy this need for
additional information, you can replace the default field value
with a call to the provided field() function. For example:

As shown above, the MISSING value is a sentinel object used to
detect if the default and default_factory parameters are
provided. This sentinel is used because None is a valid value
for default. No code should directly use the MISSING
value.

default: If provided, this will be the default value for this
field. This is needed because the field() call itself
replaces the normal position of the default value.

default_factory: If provided, it must be a zero-argument
callable that will be called when a default value is needed for
this field. Among other purposes, this can be used to specify
fields with mutable default values, as discussed below. It is an
error to specify both default and default_factory.

init: If true (the default), this field is included as a
parameter to the generated __init__() method.

repr: If true (the default), this field is included in the
string returned by the generated __repr__() method.

compare: If true (the default), this field is included in the
generated equality and comparison methods (__eq__(),
__gt__(), et al.).

hash: This can be a bool or None. If true, this field is
included in the generated __hash__() method. If None (the
default), use the value of compare: this would normally be
the expected behavior. A field should be considered in the hash
if it’s used for comparisons. Setting this value to anything
other than None is discouraged.

One possible reason to set hash=False but compare=True
would be if a field is expensive to compute a hash value for,
that field is needed for equality testing, and there are other
fields that contribute to the type’s hash value. Even if a field
is excluded from the hash, it will still be used for comparisons.

metadata: This can be a mapping or None. None is treated as
an empty dict. This value is wrapped in
MappingProxyType() to make it read-only, and exposed
on the Field object. It is not used at all by Data
Classes, and is provided as a third-party extension mechanism.
Multiple third-parties can each have their own key, to use as a
namespace in the metadata.

If the default value of a field is specified by a call to
field(), then the class attribute for this field will be
replaced by the specified default value. If no default is
provided, then the class attribute will be deleted. The intent is
that after the dataclass() decorator runs, the class
attributes will all contain the default values for the fields, just
as if the default value itself were specified. For example,
after:

Field objects describe each defined field. These objects
are created internally, and are returned by the fields()
module-level method (see below). Users should never instantiate a
Field object directly. Its documented attributes are:

name: The name of the field.

type: The type of the field.

default, default_factory, init, repr, hash,
compare, and metadata have the identical meaning and
values as they do in the field() declaration.

Other attributes may exist, but they are private and must not be
inspected or relied on.

Returns a tuple of Field objects that define the fields for this
dataclass. Accepts either a dataclass, or an instance of a dataclass.
Raises TypeError if not passed a dataclass or instance of one.
Does not return pseudo-fields which are ClassVar or InitVar.

Converts the dataclass instance to a dict (by using the
factory function dict_factory). Each dataclass is converted
to a dict of its fields, as name:value pairs. dataclasses, dicts,
lists, and tuples are recursed into. For example:

Converts the dataclass instance to a tuple (by using the
factory function tuple_factory). Each dataclass is converted
to a tuple of its field values. dataclasses, dicts, lists, and
tuples are recursed into.

Creates a new dataclass with name cls_name, fields as defined
in fields, base classes as given in bases, and initialized
with a namespace as given in namespace. fields is an
iterable whose elements are each either name, (name,type),
or (name,type,Field). If just name is supplied,
typing.Any is used for type. The values of init,
repr, eq, order, unsafe_hash, and frozen have
the same meaning as they do in dataclass().

This function is not strictly required, because any Python
mechanism for creating a new class with __annotations__ can
then apply the dataclass() function to convert that class to
a dataclass. This function is provided as a convenience. For
example:

Creates a new object of the same type of instance, replacing
fields with values from changes. If instance is not a Data
Class, raises TypeError. If values in changes do not
specify fields, raises TypeError.

The newly returned object is created by calling the __init__()
method of the dataclass. This ensures that
__post_init__(), if present, is also called.

Init-only variables without default values, if any exist, must be
specified on the call to replace() so that they can be passed to
__init__() and __post_init__().

It is an error for changes to contain any fields that are
defined as having init=False. A ValueError will be raised
in this case.

Be forewarned about how init=False fields work during a call to
replace(). They are not copied from the source object, but
rather are initialized in __post_init__(), if they’re
initialized at all. It is expected that init=False fields will
be rarely and judiciously used. If they are used, it might be wise
to have alternate class constructors, or perhaps a custom
replace() (or similarly named) method which handles instance
copying.

The generated __init__() code will call a method named
__post_init__(), if __post_init__() is defined on the
class. It will normally be called as self.__post_init__().
However, if any InitVar fields are defined, they will also be
passed to __post_init__() in the order they were defined in the
class. If no __init__() method is generated, then
__post_init__() will not automatically be called.

Among other uses, this allows for initializing field values that
depend on one or more other fields. For example:

One of two places where dataclass() actually inspects the type
of a field is to determine if a field is a class variable as defined
in PEP 526. It does this by checking if the type of the field is
typing.ClassVar. If a field is a ClassVar, it is excluded
from consideration as a field and is ignored by the dataclass
mechanisms. Such ClassVar pseudo-fields are not returned by the
module-level fields() function.

The other place where dataclass() inspects a type annotation is to
determine if a field is an init-only variable. It does this by seeing
if the type of a field is of type dataclasses.InitVar. If a field
is an InitVar, it is considered a pseudo-field called an init-only
field. As it is not a true field, it is not returned by the
module-level fields() function. Init-only fields are added as
parameters to the generated __init__() method, and are passed to
the optional __post_init__() method. They are not otherwise used
by dataclasses.

For example, suppose a field will be initialized from a database, if a
value is not provided when creating the class:

It is not possible to create truly immutable Python objects. However,
by passing frozen=True to the dataclass() decorator you can
emulate immutability. In that case, dataclasses will add
__setattr__() and __delattr__() methods to the class. These
methods will raise a FrozenInstanceError when invoked.

There is a tiny performance penalty when using frozen=True:
__init__() cannot use simple assignment to initialize fields, and
must use object.__setattr__().

When the dataclass is being created by the dataclass() decorator,
it looks through all of the class’s base classes in reverse MRO (that
is, starting at object) and, for each dataclass that it finds,
adds the fields from that base class to an ordered mapping of fields.
After all of the base class fields are added, it adds its own fields
to the ordered mapping. All of the generated methods will use this
combined, calculated ordered mapping of fields. Because the fields
are in insertion order, derived classes override base classes. An
example:

If a field() specifies a default_factory, it is called with
zero arguments when a default value for the field is needed. For
example, to create a new instance of a list, use:

mylist:list=field(default_factory=list)

If a field is excluded from __init__() (using init=False)
and the field also specifies default_factory, then the default
factory function will always be called from the generated
__init__() function. This happens because there is no other
way to give the field an initial value.

This has the same issue as the original example using class C.
That is, two instances of class D that do not specify a value for
x when creating a class instance will share the same copy of
x. Because dataclasses just use normal Python class creation
they also share this behavior. There is no general way for Data
Classes to detect this condition. Instead, dataclasses will raise a
TypeError if it detects a default parameter of type list,
dict, or set. This is a partial solution, but it does protect
against many common errors.

Using default factory functions is a way to create new instances of
mutable types as default values for fields: